Note that we can only compare macroaveraged recall for SENSEVAL-1 systems, since the sense of each individual test instance output by the SENSEVAL-1 participating systems is not available.


In this section, we propose a method for resolving this conflict: we introduce an error-driven rule pruning algorithm that considers rules induced by the coreference classifier and discards those that cause the ruleset to perform poorly with respect to the global, clustering-level coreference scoring function.

all (positive and negative) training instances formed with NP¨£ as the anaphor are no longer needed and are subsequently removed from the training data.


before spelling out our approach, let us do a sanity check.


This transformation model is really a PCFG with unusual parameterization.

the graph need not be confined to edit distance transformations, or to the simple features of table 2 (used here for comparability with the Markov models), which condition a transformation's probability on local context.


As a rough approximation to determining this kind of structure, we tagged each word according to whether it appeared in the first quarter, last quarter, or middle half of the document 14 .

one area of research concentrates on classifying documents according to their source or source style, with statistically-detected stylistic variation (Biber, 1988) serving as an important cue.


In this paper we only address the translation model and assume that there exists a one-to-one alignment from target to source words.

we used we also assessed the gains from using part of speech tags in the alignment probabilities according to the model described in section 5.2.


It is worth noting that the STRAND classifier and the tsim classifier disagreed 15% of the time on the test set.

another use of translation detection might be as an aid in alignment tasks at any level.


Space does not permit a full discussion, though it is worth mentioning that, as in the noiseless experiment, UTL outperformed the others, likewise MWBM outperformed competitive linking.

the success of the statistical approach to translation (and also to other multilingual applications that utilize parallel text) hangs crucially on the quality, quantity, and diversity of data used in parameter estimation.


In practice, the instances of this class of error are all cases where the computer can't detect the error for certain.

while it might make certain things easier and probably will improve the results of most algorithms, those improved results will not be valid for those same algorithms trained on other, non-perfect data; the vast majority of corpora will still be noisy.


We describe and evaluate the application of a spectral clustering technique (Ng et al., 2002) to the unsupervised clustering of German verbs.

pearson correlation between corresponding elements of the Gram matrices has been suggested as a measure of agreement between a clustering and a distance measure (Christianini et al., 2002).


And Marcu (2001) extracts phrase translations from automatically aligned corpora and uses them in conjunction with a word-for-word statistical translation system.

the model chose to give more weight to the second hypothesis, while preserving some probability mass for the first one.


We eventually settled for the model discussed here because it produces better translations during decoding.

since formula (4) overestimates the numerator and denominator equally, the approximation works well in practice.


This demonstrates the value of finding decoding procedures matched to the performance criterion of interest.

we stress that the MBR decoding procedures described here do not preclude the construction of complex MT models that incorporate linguistic features.


x: evenirà Figure 1: Example of a prediction for English to French translation.

we evaluated the predictor for English to French translation on a section of the Canadian Hansard corpus, after training the model on a chronologically earlier section.


Ideally, a model for p(a|x, h, s, k) would take into account whether the user actually reads the proposal before accepting or rejecting it, eg: where r is a boolean "read" variable.

because of this, we adopted a fairly radical search strategy that involves first finding the most likely sequence of words of each length, then calculating the benefit of each of these sequences to determine the best proposal.


(This process is described later in section 3.1.1.)

for instance, it is not obvious without domain knowledge that "Given a and b as in the theorem statement" matches "a=0" and "b=0", nor that "their product" and "a * b" are equivalent.


standing come into play in determining relative salience.

the hit list is evaluated for correctness, whose result is used to define the reward function as follows: where s = δ(s, a).


Librarians and search professionals have traditionally favored Boolean keyword search systems, which, when successful, return a small set of relevant hits.

we adopted the Q learning paradigm (watkins, 1989;Mitchell, 1997) to model our problem as a set of possible states, S, and a set of actions, A, which can be performed to alter the current state.


Figure 1 depicts the overall design of our speechdriven text retrieval system, which consists of speech recognition, text retrieval and query completion modules.

section 2 clarifies problems addressed in this paper.


Given a spoken query, we generate a transcription and detect OOV words through speech recognition.

we also extract terms from transcribed queries using the same method.


Kupiec et al's method, which is based on word recognition and accepts only short queries, derives multiple transcription candidates (i.e., possible word combinations), and searches a target collection for the most plausible word combination.

our task is to select the 2 maximizing the value computed by this equation as the corresponding term for ooV word based on the probability theory.


We use a DP matching method to identify the number of cases related to deletion, insertion, and substitution in  It should be noted that Equation 3is independent of the indexing method used, and therefore 2 can be any sequences of characters contained in E F .

in this case, it is possible that retrieved documents contain the word "kankitsu (citrus)."


(1994) and our method are similar in the sense that both methods use target collections as language models for speech recognition to realize open-vocabulary speech-driven retrieval.

in the case of query completion, terms that are included in top-ranked documents need to be retrieved.


There is no significant difference between the F1 scores of our method and the § ¡ ¦ £ ¥ § ¦ © & method with hierarchical structure.

performance on the original training set using SVMs is 0.285 and using NB is 0.519, so this is a difficult learning task and generalization to the test set is quite reasonable.


As in previous research, we use category hierarchies.

the computation of the former is far more efficient than the latter.


We also demon-Building: theatre store cathedral temple palace penitentiary academy houses school mansions Event: ambush assassination uprisings sabotage takeover incursion kidnappings clash shoot-out Human: boys snipers detainees commandoes extremists deserter narcoterrorists demonstrators cronies missionaries Location: suburb Soyapango capital Oslo regions cities neighborhoods Quito corregimiento Time: afternoon evening decade hour March weeks Saturday eve anniversary Wednesday Weapon: cannon grenade launchers firebomb car-bomb rifle pistol machineguns firearms Figure 9: Example Semantic Lexicon Entries strated that learning multiple semantic categories simultaneously improves the meta-bootstrapping algorithm, which suggests that this is a general observation which may improve other bootstrapping algorithms as well.

not all territories are the same size, since some categories have more members than others.


Finally, we have implemented our own relation extractor, based on Grefenstette's SEXTANT (Grefenstette, 1994), which we describe below as an example of the NLP system used to extract relations from the raw text.

much of the existing work on thesaurus extraction and word clustering is based on the observations that related terms will appear in similar contexts.


The simplest evaluation measure is direct comparison of the extracted thesaurus with a manuallycreated gold standard (Grefenstette, 1994).

the Spearman rank-order correlation coefficient is the linear correlation coefficient between the rankings of elements of A and B. R s is a useful non-parametric comparison for when the rank order is more relevant than the actual values in the distribution.


This has implications for representation sparseness and noise that a larger corpus helps to overcome, which in turn, affects the performance of ensemble methods against individual classifiers.

the added constraints reduce the number of contextual relations extracted from each sentence, leading to data sparseness.


As can be seen from Table  4, the SuperARV LM produces the best reduction in WER with Chelba's LM the second best.

n i=1 P (wi, ti|w i−1 1 , t i−1 1) (2) Johnson (2001) and Lafferty et al.


For example, the three headwords 治 療~専 念~全 快 (chiryou 'treatment' ~ sennen 'concentrate' ~ zenkai 'full recovery') form a trigram with very strong semantic dependency.

we can then introduce our second assumption: headwords in a trigram are permutable.


Perplexity is widely used due to its simplicity and efficiency.

the resulting model is similar to a 5-gram model.


In ATR model, the probability of each word in a sentence is determined by the preceding content and function word pair.

for predictive clustering models, we tried to minimize the perplexity of the training data of Letting N be the size of the training data, we have is independent of the clustering used.


Typically, when deploying a question answering system, there is some cost associated with returning incorrect answers to a user.

once the set of query reformulations has been generated, each rewrite is formulated as a search engine query and sent to a search engine from which page summaries are collected and analyzed.


the final score for an n-gram is based on the weights associated with the rewrite rules that generated it and the number of unique summaries in which it occurred.


Many of the causes listed above are related to verb phrases.

overall, however, the dependency representation has the best cohesion properties.


Recall that in cases of annotator disagreement, the P alignment is taken to be the union of the P alignments of both annotators.

figure 1) and all of the modal-related crossings (e.g.


This causes the phrasal translation heuristic to fail resulting in a crossing where there should be none.

in an SMT system which is using some type of phrasal movement during reordering, dependency structures should produce better results than raw parse trees.


We generated data suitable as input to a machine learning algorithm from our corpus using a straightforward algorithm which combined potential anaphors and their potential antecedents.

they only used pairs of anaphors and their closest antecedents as positive examples in training, but evaluated according to Vilain et al.


Accompanying experimentation revealed that J48's performance is only slightly inferior to that of C5.0 for our data.

only a defined subset of all possible reference resolution cases was considered relevant in the MUC-5 task description, e.g., only entity references.


This is more or less a problem with the annotation standard used for marking up voicemail transcripts.


4 holds has the following interesting interpretation: assuming a bijection s that is complete i.e., all source and target positions are covered, an efficient block segmentation algorithm exists to compute a Viterbi block alignment as in Figure 1 for a given training sentence pair.

yamada and Knight (2002) presents a decoder for syntax-based MT that uses so-called phrasal translation units that correspond to blocks.


the pair (e ,'Xinhua news agency') is learned from the training data.

we compute the probability of a block sequence ¢ ¡ £ .


Similarly, on the HUB-4NE test set, using (4) yields 736, while (5) yields 727.

a much bigger and more reliable comparable corpus may be used, and eventually more accurate trigger-pairs will be acquired.


Even though our method ignores this piece of information, we used it to derive manually "ideal" clusters.

it is important to know how sensitive our overall algorithm is to the quality of the identified clusters.


When the documents in the collection belong to the same domain and genre, the fragment mapping takes advantage of the topical structure of the texts.

both of these methods construct an alignment by looking at sentences one at a time, independently of the decisions made about other sentences.


Our main claim is that that the pre-processing approach, coupled with a lexicalized parser outperforms both state-of-the-art postprocessing and in-processing.

the algorithm searches from left to right for a possible candidate, preferring non-adjuncts over adjuncts.


This is the deep-syntactic subcategorization frame.

we also show that predicting labels from a "lightweight" parser that generates deep syntactic features performs comparably to using a full parser that generates only surface syntactic features.


For example, in the sentence: "Alexandra bent her head;" "Alexandra" is an external argument Noun Phrase, "bent" is a target predicate, and "her head" is an object argument Noun Phrase.

nP, PP) is extracted from the parse tree of the sentence by finding the constituent in the tree whose boundaries match the human annotated boundaries of the element.


The impact of our head-word based scoring is analyzed in Table 3, which compares results when only the head word must be correctly identified (as in Table 2) and to results when both the beginning and end of the argument must be correctly identified in the sentence (as in Gildea and Palmer (2002)).

the performance of our CCG system is lowered by the fact that the syntactic analyses in its training corpus differ from those that underlie PropBank in important ways (in particular in the notion of heads and the complement-adjunct distinction).


In this section, we describe the countability classes, the resources used in this research, and the feature extraction method.

for an m × n-valued twodimensional feature cluster, we generate a total of 5mn + 3(m + n) independent feature values.


There are about 60000 samples in each slice in average.

the derived tree for Example 1 is shown in Fig.


2 We illustrate the LTAG formalism using an example.

to use all possible n-gram features typically introduces too many noisy features, which can result in lower accuracy.


So our algorithm is effectively Ste applied to coreference resolution -instead of two parsing algorithms that correspond to different features, we use two learning algorithms, each of which relies on the same set of features as in G&Z.

in this paper, k = 2 and we set α to 0.01.


co-training algorithms are fundamentally different.

the clustering algorithm, however, is not manipulated by the bootstrapping procedure.


In contrast, our ranking method is more conservative in that it places more emphasis on maintaining labeled data accuracy than the B&M method.

anaphora resolution (Mueller et al., 2002)).


Table 1: The justifications (shown in the right column) for using naive Bayes and decision list learner as the underlying learning algorithms for bootstrapping coreference classifiers are based on the corresponding observations on the coreference task and the features used by the coreference system in the left column.

the fact that most NP pairs in a document are not coreferent implies that the negative instances grossly outnumber the positives.


Finally, if the cross-entropy values for the experiment set are larger than for the control set, we can infer that the values for that semantic cluster do influence the text.

the resulting information can be later either further filtered, ordered and augmented by later stages in the generation pipeline (e.g., see the spreading activation algorithm used in ILEX (Cox et al., 1999)).


We refer to this extension using 2-norm penalty as the 2-norm inequality ME model.

the larger the widths, the more the feasible region is enlarged.


The solution for the inequality ME model would become sparse if the optimization determines many features as inactive with given widths.

the dual objective function becomes: Accordingly, the gradient becomes: It can be seen that this model is a natural combination of the inequality ME model and the Gaussian MAP estimation.


There are training and test data sets and the training data consists of about 7200 sentences.

in this paper, we varied the scale (exponential vs logarithmic) and the manner of the optimization (sequential vs pointwise) and using different combinations, we designed 4 different objective functions.


Figure 2 shows this property of exp-loss in contrast to log-loss in a binary classification problem.

as in adaBoost, a distribution over observations is defined:    Update % 10: until stopping criteria as it can be seen from Line 4 in algorithm 2, the feature that was added to the ensemble at each round is determined by a function of the gradient of the sequential exp-loss function (Eq.


In this new algorithm, instead, we only compute the approximate gains for the top-ranked features based on the models obtained from previous stages.

in Figure 3, the iFS algorithm stops after 1000 features are selected.


A preliminary study in (Emami et al., 2003) already showed that this approach is promising in reducing the word error rate of a large vocabulary speech recognizer.

the interpolation weight with the trigram is 0.4 and 0.5 respectively, for the Kneser-Ney smoothed SLM and neural network based SLM.


Figure 4: Learning curves As we expected, the learning curve of the training data in EM iteration 1 is not as smooth as that in EM iteration 0, and even more so for the heldout data.

the notation "En" indicates the models were obtained after "n" iterations of EM training 1 .


Lexical semantic information can be extracted from existing dictionaries such as WordNet.

the sum constraint ensures that the update is balanced, which is crucial to guaranteeing the convergence of the learning procedure (cf.


Third, the linguistically motivated patterns we use reduce long-distance dependencies between anaphor and antecedent to local dependencies.

it can classify all antecedents as "antecedent no".


nik, 1995) while 'svm2' uses a modified SVM for unbalanced data (Morik et al., 1999), which gives a large penalty to misclassification of a minority (= positive) example.

the subject of yomu is accepted if and only if it is AGENT or PEOPLE.


Our method is supervised in that the training data is manually labeled.

the paper studies the problem of parsing Chinese unsegmented sentences.


It is clear that character-level tags encode word boundary information, and chunk-level 1 labels are word-level POS tags.

we break features used in this study into two categories.


For the rest of this section, we limit our discussion to the comparison of the results of the original SVM and SVM with four virtual examples per SV (SVM with 4 VSVs).

the scores in bold means that the score of SVM with 4 Virtual SVs per SV is better than that of the original SVM (shown in table 4).


Corpus-based supervised learning is now a standard approach to achieve high-performance in natural language processing.

the label of a virtual example is given from the original document.


SVM with 4 VSVs still outperforms the original SVM significantly.

we focus on virtual examples with Support Vector Machines, introduced by Vapnik (1995).


Our formulation uses a factorization analogous to the standard dynamic programs for parsing.

intuitively, the magnitude of α i,y is proportional to the attention we must pay to that mistake in order not to make it.


He used a beam search to find multiple parses.

to get complete coverage, they required 1760 popped edges, which is a factor of 37 greater than the minimum.


We estimated the N with a similar method.

this research was partly supported by NSF grant #EIA-0205111.


In terms of the relations we recognize, it can be stated that , and antonym(V 1 , V 2 ).

dIRT only outputs pairs of paths that have some semantic relation.


Our results indicate that humans achieve an average accuracy of about 77.45% to 88.58%, where some domains seem to be easier than others.

the segment granularity is more fine-grained for paragraphs than for topics.


For example, the worst case training time for SVMs is quadratic in the number of features and this method reduced the dimensionality to 3% of its original size.

the classification algorithm used here is a variant of Random Forests (RFs) (Breiman, 2001).


Each frame provides a set of predicates (nouns, verbs or adjectives) which can introduce the frame.

this reduces our frame sample from 196 to 40.


Nevertheless, both models correlate strongly with each other in both evaluation modes (¡ .001 for overlap).

if we only had the three sentences in (1) for the iMPACT corpus, the three occurring patterns would be {(impactee, EXT), (impactor, COMP)}, {(impactor, EXT), (Result, COMP)}, and {(impactors, EXT), (Place, MOD)}.


In this paper, we have performed an error analysis for semantic role assignment, concentrating on the relationship between argument structure and semantic role assignment.

we turn the cost function into a "quality" function.


We describe a model for creating word-to-word and phrase-to-phrase alignments between documents and their human written abstracts.

figure 2 shows the schematic drawing of the PBHMM constructed for the document "a b".


The clustering algorithm chosen is based on a decision-tree approach inspired by the Semantic Classification Tree method proposed in (Kuhn and Mori, 1995) and already used for corpus clustering in (Esteve et al., 2001) and (Bechet et al., 2003).

we use here, to build the vectors encoding the test corpus, automatic calltypes calculated by the Spoken Language Understanding Module.


Note that the sense and SCF variables are highly correlated for this verb.

(For the Senseval-2 corpus, this collection includes not only the words in the sentence in which the verb occurs, but also the words in surrounding sentences.)


We use simple Laplace add-one smoothing to smooth the distribution.

2 Instead, there are some corpora such as SemCor and Senseval-2 labeled for sense, and others that are parsed and from which it is possible to compute verb SCFs deterministically.


Commonly PCFGs are learned using MLE of rewrite rule probabilities from large sets of treeannotated sentences.

the bucket weights are found empirically using a simple optimization procedure on k-fold training set accuracy.


In addition, many of the variants do not contain useful information for our task: for example, we wouldn't expect to get much value from knowing about the presence or absence of an adverb in the phrase.

note that the gap between training set cross-validation and test set performance on the SCF determination task persists (although it is diminished slightly).


The frequencies for attributes and values were again collected as in the first experiment.

the input to clustering is a frequency table with concepts as rows and values, attributes, or both attributes and values as columns.


This feature allows the user to fragment the search space into a number of periods, hence retrieving only pages that have been updated during a specified period.

these data were used in a different way.


Another is phrase structure category information associated with the nodes, which summarizes the values of several feature paths and is available in the Redwoods corpus as Phrase-Structure trees.

the feature map obtains matching of substrings of the input string to features, allowing the occurrence of gaps.


The prevailing view of the NLP community had been that bilexical statistics were sparse, and Gildea (2001) adopted this view to explain his results.

understanding what is learned most well can shed light on the types of features or dependencies that are most efficacious, pointing the way to new features of that type.


This practice could be made more rigorous by relying on distributional similarity.

the work of Gildea (2001) showed that, with an approximate reimplementation of Collins' Model 1, removing all parameters that involved dependencies between a modifier word and its head resulted in a surprisingly small decrease in overall parse accuracy.


This can be explained by the fact that it is much more   Table 3: MDI2BCache test perplexities.

extra training time is negligible in the first model.


For nouns and noun phrases plural forms and entries with definite and indefinite determiners were generated.

a natural way of integrating them together is a geometric interpolation shown in (10) or equivalently a linear interpolation in the log domain.


Allowing mto-n matching of up to two nodes on either side of the parallel treebank allows for limited nonisomorphism between the trees.

allowing non-1-to-1 correspondences between nodes in the two trees is necessary to handle the fact that the depth of corresponding words in the two trees often differs.


The selection of the slots is based on our linguistic intuition -for example it is reasonable to assume that the information about part-of-speech and the agreement features (gnc) of previous words should help in prediction of the same slots of the current word; or information about part-of-speech, case and person should assist in determining person.

we use a paradigm-based morphology that avoids the need to explicitly create a large lexicon.


Both filters (the longest-ending filter and automatically acquired lexicon) reduce the ambiguity significantly; the former producing a considerable drop of recall, the latter retaining high recall.

in addition, we specify that a few long but very rare endings should not be included in the maximum length calculation (e.g., 2nd person pl.


We therefore have two strategies for reducing ambiguity.

not all morphological attributes are useful for predicting the attributes of the succeeding word (e.g., tense is not really useful for case).


Consequentially, there is no choice but to select a short path (single token) in order to maximize the whole sentence probability.

if we use the top level, we lack in granularity of POS to capture fine differences.


The revised list contained exactly 3,580 BP stems.

we looked for simpler but more effective ways to improve the algorithm.


The CHUNK row in the results table is the performance of an optimally performing NP chunker.

the optimal tagging is given by back-tracing through A, beginning at A n,0,t for any tag t. Even for long sentences, this algorithm requires very little time and memory.


In tallying frequencies we treated all numeric expressions as occurrences of a special term, "*num*".

at the same time, the improvement is fairly consistent at all training set sizes.


Using the wildcards listed in Table 2, we trained BWI for 500 boosting iterations on each of the seven entity fields.

we might expect that the end of a name, say, would be easier to identify if we know where it begins.


Here, the specific dependence assumptions are that the conditional probabilities can be represented by a POS trigram model p(g n |g n−1 n−2 ) and a word membership model p(w n |g n ).

for the generative model, both decision rules yield similar results.


Theoretically, we should be able to just port corpus-based, machine learning techniques across different languages since the techniques are largely language independent.

theoretically, we should be able to just port corpus-based, machine learning techniques across different languages since the techniques are largely language independent.


One problem with evaluating a spell checker designed to correct search queries is that evaluation data is hard to get.

we present an approach that uses an iterative transformation of the input query strings into other strings that correspond to more and more likely queries according to statistics extracted from internet search query logs.


For learning algorithms to identify these topics, a text is usually represented as a bag-of-words, where a text is regarded as a multi-set (i.e., a bag) of words and the word order or syntactic relations appearing in the original text is ignored.

is a normalized weight assigned to x i .


When a convolution kernel is applied to sparse data, kernel dot products between almost the same instances become much larger than those between different instances.

first, the algorithm starts with a set of trees consisting of single nodes, and then expands a given tree of size (k − 1) by attaching a new node to this tree to obtain trees of size k. it would be inefficient to expand nodes at arbitrary positions of the tree, as duplicated enumeration is inevitable.


These graphs indicate that high-precision predictions can be made for the top-level of the verb hierarchy, as well as verbs Request and Deliver, if one is willing to slightly reduce recall.

this problem also raises a number of interesting research issues.


Prior research on machine learning for text classification has primarily considered classification of documents by topic (Lewis, 1992;Yang, 1999), but also has addressed sentiment detection (Pang et al., 2002;Weibe et al., 2001) and authorship attribution (e.g., Argamon et al, 2003).

while a proposal is associated with a new task, an amendment is a suggested modification of an already-proposed task.


The second finding concerns the question whether the entropy rate effect generalizes to sentences with a position of greater than 25.

this means that a small but reliable entropy effect can be observed even on the raw data, i.e., for individual sentences rather than for bins of sentences with the same position.


This result is perhaps not surprising, as a lot of the data is very sparse: for positions between 77 and 149, less than ten data points are Let us now turn to Table 1, which displays the results that were obtained by computing correlation coefficients on the raw data, i.e., without computing the mean entropy for all sentences with the same position.

genzel and Charniak (2002) show that this prediction holds for caching language models such as the ones proposed by Kuhn and de Mori (1990).


As we argued in Section 2, this is predicted by the entropy rate principle: the optimal way to send information is at a constant rate.

this cut-off has been set so that there are at least 10 items in the test set for each position.


The principle states that the entropy rate in a text is constant, i.e., that speakers produce sentences so that on average, all sentences in a text have the same entropy.

as the eye-tracking corpus we used was a corpus of connected sentences, it enabled us to also test another prediction of the entropy rate principle: in context, all sentences should be equally difficult to process, as speakers generate sentences with constant informativeness.


In order to reveal more about the cause of improvements, we also compared the KN-trigram and RF-trigram on events that are seen in different number of DTs.

since the length of the history could be very large, a better smoothing method would be very useful.


All of the experimental results in this section are based on this corpus and setup.


It is this kind of observations that motivate the addition of linguistic knowledge as a vehicle for combining the results of the classifiers.

it must be noted that the scoring function alone is not a function for extracting roots from Hebrew words.


Here, we trained the system as above but we tested it on different words whose roots were known to be in the training set.

the irregular roots n.p.l, i.c.g, q.w.m and g.n.n in this pattern yield the seemingly similar forms hplh, hcgh, hqmh and hgnh, respectively.


Obviously, we wouldn't want any of the sentences in the unrelated document to be included in a generic summary of the cluster.

when computing degree centrality, we have treated each edge as a vote to determine the overall prestige value of each node.


In this paper, we propose a novel approach using a maximum entropy model for NE alignment.

a translated Chinese NE may appear at a different position from the corresponding English NE in the sentence.


Finally the one with a higher frequency in the whole corpus, say, "中国", will be viewed as the final alignment for "China".

for example, in figure 1, if "China" only has a translation probability over the threshold value with "中", the two seed data are located with the index of 0 and 4.


On the other hand, IBM Models allow at most one word in the source language to correspond to a word in the target language (Koehn et al., 2003;Marcu, 2001).

for example, in the following sentence pair with word segmentation, the English NE "United States" can no longer be correctly aligned to "美 国".


(2003) proposed to extract Named Entity translingual equivalences based on the minimization of a linearly combined multi-feature cost.

to avoid error propagations from word segmentation and NER, we directly extract Chinese NEs and make the alignment from plain text without word segmentation.


Section 2 introduces WORDNET DOMAINS, the lexical resource that provides the underlying knowledge to the DRE technique.

these vectors are defined in a multidimensional space, in which each domain represents a dimension of the space.


The performances obtained for nouns are sensibly higher than the one obtained for verbs, confirming the claim that domain information is crucial to disambiguate domain words.

given a certain domain, DRE considers frequency scores for both relevant and non-relevant texts (i.e.


can be obtained by integrating over the distribution.

from this test result, we would like to conclude what the true translation performance is.


Documents are retrieved and the top K are further processed.

several statistical approaches have proven to be successful in answer extraction.


The retrieval step, which is very critical in the QA process, does not take full advantage of context information.

each training question is represented as a data point in this space.


One of us then used the Kuwait consensus agreement to annotate the 16 machine summaries for that text which were created by different participants in DUC-2002, an annotation which could be done rather quickly.

voorhees (2000) shows that the relative rankings of IR systems are stable across annotators even though relevance judgements differ significantly between humans.


Model g is a reduced model with the statistics of the decoder without using the correct translation WFST.

wand and waibel (1997) and Tillmann and Ney (2003) proposed breadth-first search methods, i.e.


Unlike ordinary FSA minimization, states are merged without considering their successor states.

few studies have focued on the integration of each WFST submodel to improve the decoding efficiency of machine translation.


We proposed a method to compile statistical models to achieve efficient decoding in a machine translation system.

wFST optimization operations such as determinization are nearly impossible to apply to wFSTs in machine translation because they are much more ambiguous than speech recognition.


Those data are confirmed biographical information and do not bear the necessary contradiction associated with evolving news stories.

the scandal element from our corpus study (Section 3) is sufficient to identify information of the disputed kind.


Many approaches to summarization can be very broadly characterized as TERM-BASED: they attempt to identify the main 'topics,' which generally are TERMS, and then to extract from the document the most important information about these terms (Hovy and Lin, 1997).

the kth sentence chosen is the one with the largest index value in the kth right singular vector in matrix The summarization method proposed by Gong and Liu has some disadvantages as well, the main of which is that it is necessary to use the same number of dimensions as is the number of sentences we want to choose for a summary.


There are numerous theoretical approaches describing IS and its semantics (Halliday, 1967;Sgall, 1967;Vallduví, 1990;Steedman, 2000) and the terminology used is diversesee (Kruijff-Korbayová and Steedman, 2003) for an overview.

applying these rules for the sentence (b) in example (1) we find the Topic and the Focus of the sentence: It is worth mentioning that although most of the time, CB items belong to the Topic and NB items belong to the Focus (as it happens in our example too), there may be cases when the Focus contains some NB items and/or the Topic contains some CB items.


Table 5 displays in the second column the number of all contexts.

all theories consider at least one of the following two distinctions: (i) a Topic/Focus 1 distinction that divides the linguistic meaning of the sentence into parts that link the sentence content  We use the Praguian terminology for this distinction.


The accuracy of Rules 1and 3varies depending on the type and direction of the particular relation shared by the two noun phrases.

tables 2 and 3 show the performance table 3.


In other words, the value at X=3 gives the f-measure averaged over all people who were mentioned at least thrice in the input.

as the task definition above suggests, our approach is to identify particular semantic attributes for a person, and generate a reference formally from this semantic input.


Multi-document summarization, however, has information available that was not available during the translation process and which can be used to improve summary quality.

the information theoretic approach gives significant improvement for the arbitrary NP case as well, particularly for precision, which is an indicator of grammaticality.


There is additional noise -the values AFP and former are most likely errors.

the rewrite operations increases both scores by around 0.2.


L-CS-Local-Long and L-CS-Local have the largest number of rules, 14, which is rather a small set of rules.

1 Available via http://www.link.cs.cmu.edu/link/ Link Grammar is a context-free lexicalized grammar without explicit constituents (Sleator and Temperley, 1993).


The best performance is generated by L-CS-Local-Long.

the experimental results on a dictation data corpus show that linguistic features alone are not as useful as word confidence scores in detecting errors.


Successfully detecting SR errors can speed up the entire process of error correction.

it boosts CER by a relative improvement of 12.30% over CS and a relative improvement of 7.02% over L-CS-Local.


Hence, the linguistic features can improve the system's ability in finding more errors.

we focus on error detection in this study.


In contrast, our PMI measure was computed based on a general sample of the World Wide Web, which was not tailored to the audio corpus used to evaluate its performance.

figures 3 and 4 show how the content word error rate (cWER), the percentage of lost good words (%Lost), and the f-measure vary as we apply more and more aggressive error filtering (by increasing K) to both datasets.


It is the first in a series of experiments aiming to leverage redundancy across a large fact base extracted from text, to improve the quality of extracted data.

we aim to use a much larger set of data to train the parameters, without manually tagging large training sets.


For example, we estimate the conditional probability of linking not to ne...pas by considering the number of sentence pairs in which not occurs in the English sentence and both ne and pas occur in the French sentence, compared to the number of times not is linked to both ne and pas in pairs of corresponding sentences.

since evaluating each combination of parameter values in this way can take hours to days on a large training corpus, it seems safe to say that these parameters are rarely if ever truly jointly optimized for a particular alignment task.


Segmentation features discussed above result in both suffix and prefix features as well as stem features.

these features fire when the previous source and target word pair are linked.


The truth data was created by asking NIST assessors (the humans performing this task) to identify the set of relevant sentences from each relevant document and then from that set of relevant sentences, mark those that were novel.


We have shown the effects of ASR output in the news stream, and demonstrated a procedure to alleviate the problem.

the first story on this topic is the story that first carries the report on the earthquake's occurrence.


Improving NED by better comparison of stories was the focus of following papers.

the system reported with a high confidence score that Story 3 is old.


Some works (McCallum and Nigam, 1999;Liu et al., 2004) performed an additional Expectation Maximization algorithm over the training data, but reported rather small incremental improvements that do not seem to justify the additional effort.

additional seeds, beyond the category names, were identified by two lexicographers.


instances that share coherent features) are represented by similar vectors in the reduced dimensionality space.

the complete Reuters collection includes 12,902 documents for 90 categories, with a fixed splitting between training and test data (70/30%).


Finally, following the single-labeled categorization setting of Step 1 in the IL scheme, the most likely category is assigned to each instance, that is, argmax C i P (C i |t j ).

(a) utilizing a Latent Semantic Space to obtain better similarity assessments between seeds and examples, and (b) applying a Gaussian Mixture (GM) algorithm, which provides a principled unsupervised estimation of classification probability.


Vishwanathan and Smola (2004) presented the O(|T 1 | + |T 2 |) procedure that exploits suffix trees to speed up the calculation of tree kernels.

for K r c , the factors were about 1.3, 2.1, and 2.0, respectively.


There are many possible structures for which tree kernels can be defined.

the FREQTM conversion for marked labeled ordered trees might be made faster by exploiting the mark information for pruning.


Lin (2004a;2004b) and Lin and Och (2004) proposed an LCS-based automatic evaluation measure called ROUGE-L.

in terms of how much of the content of % t can be reproduced by using only sentences in º .


However, N-gram based methods have a critical problem; they cannot consider co-occurrences with gaps, although the LCS-based method can deal with them.

in most cases, the performance is improved by including stop words for N (© i 5 ).


When the same set was used for training and testing the result obviously overestimates the learning capability of the framework.

when tested with a popular standard collection, our approach achieved the performance of the best well-known techniques (BM25 and language models), which have been developed as a result of extensive past experiments and elaborate theoretical modeling.


By experimenting with the other ranking functions and their parameters, we noticed that the implementation of BM25, available in Lemur, provided almost identical performance (0.204).

for each selected (sampled) pair of documents (dr, di ), such that dr is a relevant document and di is irrelevant, the classifier was presented with a positive example created from the vector of differences of features f p = f(q, dr) -f(q, di), and also with the negative example as the inverse of it: f n = f(q, di) -f(q, dr).


We show that our alignment link projection approach yields a significantly lower alignment error rate than that of the best performing alignment system (22.6% relative reduction on English-Spanish data and 23.2% relative reduction on English-Chinese data).

this process eventually identifies all possible instantiated forms of the templates.


However, if the overall system is evaluated using a metric other than precision (e.g., recall), the accuracy mechanism may not guarantee that the best rule is chosen at each step.

moreover, the differences among ALP variations themselves were statistically significant within 95% confidence interval.


The learning stops when there is no transformation that improves the current state of the data or a prespecified threshold is reached.

we choose the best rule according to the evaluation metric to be used for the overall system.


During the last years there has been growing interest in using neural networks for language modeling.

it can be shown that the outputs of a neural network trained in this manner converge to the posterior probabilities.


The strategy executes and, once completed, it is removed from the stack and the dialog resumes from where it was left off.

the Let's Go Public Bus Information System always uses explicit confirmations, in an effort to increase robustness (at the expense of potentially longer dialogs).


Our strategy is similar to that proposed in (Nöth et al., 2000), which uses categorical labels defined in terms of syntactic structure and pause duration.

while the prosody feature extraction depends on timing of the hypothesized word sequence, the acoustic cues are relatively robust to word errors and the break model can be retrained on recognizer output to automatically learn to discount the lexical evidence.


Both the table and figure show that the reranked beam achieves a consistent trend in parse accuracy improvement relative to the PCFG beam, similar to what is demonstrated by the 1-best scores (Table 3).

this work adopts their edit detection preprocessing approach.


The former measures whether humans agree in placing chunk boundaries, whereas the latter additionally measures whether humans agree in assigning chunk labels.

we carried out an Analysis of Variance (ANOVA) to examine the effect of different types of compressions (Baseline, BT-2-Step, Spade, and Human).


For example the sentence "I saw yesterday the man in the park with a telescope" has 5 different semantic interpretations based on the way the prepositional phrases "in the park" and "with the telescope" are attached: I saw yesterday [the man [in  The problem can be viewed as a decision of attaching a prepositional phrase (PP) to one of the preceding head nouns or verbs.

n1-p-distance = log 10 (1 + log 10 d n1−p ).


To return goal, one may not need to compute the values of all items.

we ran five iterations of EM on the wSJ10 corpus 26 using the Treebank grammar from that corpus.


In our experiment, garbage collection was called explicitly after each sentence and counted as part of the runtime (typically 0.25 seconds for 10-word sentences, 5 seconds for 40-word sentences).

it recognizes that a low-probability item must have high priority if it could be used as a side condition in a higher-probability parse (though this cannot happen for the side conditions derived by the magic templates transformation ( §6)).


More complex policies will kick-in progressively for those states that are encountered more often.

similarly, since the listener does not talk we do not need to define µ 10 or s 10 .


Each string is presumed to be a facet in the definition of the series' target that had not yet been covered by earlier questions in the series.

the individual points in the graphs are the data points actually computed by the procedure above, while the lines are the best-fit exponential curve for the data points in the current bin and extrapolated to size 64.


Systems participating in the TREC evaluation were required to process series independently from one another, and were required to process an individual series in question order.

the series a question belonged to, the order of the question in the series, and the type of each question (factoid, list, or Other) were all explicitly encoded in the XML format used to describe the test set.


Acknowledgements This material is based upon work sup-ported by the Advanced Research and Development Activity (ARDA) under Contract No.

we score answers truncated at length v times that of the reference answers, where v is set to be 1, 2, and 3.


Surprisingly, the relation feature has a high coverage.

it is hard to manually construct such patterns since it largely depends on the knowledge of the pattern designer.


The initial state cannot be "2" in the 3-state model; all states can be final.

existing knowledge bases like WordNet and Wikipedia, combined with intense recent work (etzioni et al., 2004) on bootstrapping is-a hierarchies, can thus lead to potentially large benefits.


We evaluate our collective content selection model in a sports domain.

multiple pairs of entries can be connected by the same link type.


The Web contains a wealth of opinions about products, politicians, and more, which are expressed in newsgroup posts, review sites, and elsewhere.

on the second task, oPINE has a precision of 86% and a recall of 89%.


Consider the underlined polarity words in the sentence below: (4) Philip Clapp, president of the National Environment Trust, sums up well the general thrust of the reaction of environmental movements: "There is no reason at all to believe that the polluters are suddenly going to become reasonable."

similarly for "polluters": in the context of the article, it simply refers to companies that pollute.


They also consider local negation to reverse polarity.

table 4 gives neutral-polar classification results for the 28-feature classifier and two simpler classifiers that provide our baselines.


For words that came from positive and negative word lists (General-Inquirer, 2000;Hatzivassiloglou and McKeown, 1997), we largely retained their original polarity, either positive or negative.

tasks such as multi-perspective question answering and summarization, opinion-oriented information extraction, and mining product reviews require sentence-level or even phrase-level sentiment analysis.


The learned patterns were applied to the test data and the extracted sources were scored against the manual annotations.

5 We evaluate performance using 3 measures: overlap match (OL), head match (HM), and exact match (EM).


Ripper reports a confusion matrix for each class; Recall, Precision, and F-measure for these classes are shown, along with their average across classes.

we now turn to the global discourse-level features.


A t-score > 1.645 is a significant difference with 95% confidence.

as an example, consider the GNIS gazetteer in Table 1, let F = {U.S. County, U.S. State, Lat-Long, and Elevation}.


This sentence states an intention of motion, not the actual action.

our goal is to devise semantic axioms for combinations of two relations, R 1 and R 2 , by observing the semantic connection between the w 1 and w 3 words for which there exists at least one other word, w 2 , such that R 1 (w 1 , w 2 ) and R 2 (w 2 , w 3 ) hold true 3 .


1 Usually a single sentence, but occasionally longer.

we believe there is still room for improvement since carefully-hand chosen weights results in comparable performance to the learned weights on the final test set.


In general, we do not know the correct answers and hence do not know f (s).

the basic method for strapping a classifier for a new task instance is very simple: 1.


His results support the idea that grouping together related senses can increase retrieval effectiveness.

variable levels of accuracy are introduced using a weighted probability model where the correct pseudosense for a given test instance is seeded with a fixed probability equivalent to the desired accuracy being simulated.


Finally, the most likely set of labels is determined by identifying for each word the label that has the highest score.

recent comparative evaluations of different variants of the Lesk algorithm have shown that the performance of the original algorithm is significantly exceeded by an algorithm variation that relies on the overlap between word senses and current context (Vasilescu et al., 2004).


This is because many concepts are specific to particular domains, and for many words their most likely meaning in context is strongly correlated to the domain of the document they appear in.

the senses of a word ¢ are each assigned a ranking score which sums over the distributional similarity scores of the neighbours and weights each neighbour's score by a WN Similarity score (Patwardhan and Pedersen, 2003) between the sense of ¢ and the sense of the neighbour that maximises the WN Similarity score.


Although the predominant sense of a word is not always the domain-specific sense in a domain-specific corpus, the domain-specific senses typically occur more than they do in non-relevant corpora.

there are exceptions: return, fan and title for FINANCE and return, half, level, running strike and share for SPORTS.


The parameter ζ in equation 7denotes the balancing factor of the Word Model and the POS Model.

the word context model and the POS context model estimate the probability of generating a word or a POS given previous context.


Additionally, phrase-based transliteration further improved the transliteration performance by a significant margin.

japanese names are often semantically translated when they contain characters borrowed from Chinese, therefore the translation cost is more important for the japanese model ( λ =0 in this case).


Our starting point is automatic speech recognizer output, and we aim to locate names in particular.

these models could detect the links between the words in the equivalence class {Katherine Kathryn Catherine}.


Names are grouped such that if P(CAT|kate) is high (above some threshold) then there is a link between CAT and kate.

the model might learn a high probability for P(CAT|kate).


A key disadvantage of the CMM is that it makes critical statements about independence that the HMM does not: the converging arrows at each tag put the parent nodes (the previous tag and the current observation) into causal competition and as a result the model states that the previous tag is independent of the current observation.

the observed-child node, c i , now has three parents: the tags s i−1 , s i , and s i+1 .


For training and testing, we used the data set provided for the CoNLL-2000 shared task.

the model gives equally good performance as the maximum entropy modeling with Gaussian priors (Chen and Rosenfeld, 1999), and the size of the resulting model is much smaller than that of Gaussian priors because most of the parameters become zero.


Thus the training typically takes four or 16 times as much time as it would take for training a single unidirectional tagger, which looks somewhat expensive.

a naive implementation of this algorithm requires O(n 2 ) invocations of local classifiers, where n is the number of the words in the sentence, because we need to update the probabilities over the words at each iteration.


A common choice for the local probabilistic classifier is maximum entropy classifiers (Berger et al., 1996).

in the first iteration, we always use the local classifiers trained with no contextual tag information (i.e.


Using more snippets always increased the overall translation accuracy, and with all the 165 snippets (on average per query), our approach achieved 80% top-1 translation accuracy, and 90% top-5 accuracy.

many key phrases are person and location names, which are phonetically translated and whose written forms resemble their pronunciations.


We estimate both and , and define the NE translation cost as:  The more often a bilingual phrase pair co-occurs, or the closer a bilingual phrase pair is within a snippet, the more likely they are translations of each other.

it is possible to discover these translation pairs through their surface strings.


follows its own strategy to maximize similarity within a cluster.

by varying adjustment factor a from 0 to 10 and higher we observed almost the entire theoretical range of Ca: starting from negligible change and going all the way to 2 , where the added terms entirely dominated the original ones.


The earlier difficulties may have resulted from the following reasons: 1) The test collections were small, sometimes only few dozens of documents.

after preliminary tests, we settled on the simple linear modification with post re-normalization as presented below.


As we remarked earlier, |H(t i,j )| is exponential in size so direct calculation of either is impractical.

in this section we will see how different definitions of the domains give rise to the three main model types: clustering, refinement, and mapping into a pre-built ontology such as WordNet.


More data should be annotated to create a treebank of morphological analyses.

there is a large number of locally optimal parameter settings to which the unsupervised training can be attracted.


In the preceding discussion, we have shown that natural language dependency parsing can be reduced to finding maximum spanning trees in directed graphs.

we performed experiments on the Czech Prague Dependency Treebank (PDT) (Hajič, 1998;Hajič et al., 2001).


... pleasure has probably been the main goal all along.

all non-humorous examples are enforced to follow the same length restriction as the one-liners, i.e.


The humorous data set consists of one-liners collected from the Web using an automatic bootstrapping process.

we investigate whether automatic classification techniques are a viable approach to distinguish between humorous and non-humorous text, and we bring empirical evidence in support of this hypothesis through experiments performed on very large data sets.


Recall that these distinctions are based on absolute performance and not gain.

the second is a derivative of a measure used to score dependency parses.


While there have been many successful applications of machine learning methods to tasks in NLP, learning algorithms are not typically designed to optimize NLP performance metrics.


To decide the optimal length of text snippets to retrieve, we carried out pilot experiments with two length settings: 250 (≈ 110 English words) and 400 (≈ 175 English words) Chinese characters, and found that more context words helped improve WSD performance (results not shown).

it is possible that the Chinese translations are also ambiguous, which can make the topic of a collection of text snippets deviate from what is expected.


our pilot experiments on other datasets (results not shown).

the first translation means "an exit" or "to export".


It is therefore necessary to translate a query into an equivalent MONA formula that re-spects the translation of the trees.

hence a corpus provider could at least in theory provide his corpus as a collection of MONA automata.


As a consequence, the choice of the syntactic annotation scheme should not reflect a particular syntactic theory but rather be as theoryneutral as possible.

tree automata are generalisations of finite state automata to trees.


We compare KNOWITNOW experimentally with the previouslypublished KNOWITALL system, and quantify the tradeoff between recall and speed.

in this stage, each document that matches a query has to be retrieved from an arbitrary location on a disk.


We present a novel architecture for Information Extraction (IE), embodied in the KNOWITNOW system, which does not depend on Web search-engine queries.

numerous modern nLP applications use the Web as their corpus and rely on queries to commercial search engines to support their computation (Turney, 2001;Brill et al., 2001).


Our recent work has described the BE search engine in detail , and also analyzed the URNS model's ability to compute accurate probability estimates for extractions .

the remainder of the paper is organized as follows.


BE's novel neighborhood index enables it to process these queries with O(k) random disk seeks and O(k) serial disk reads, where k is the number of non-variable terms in its query.

the Bindings Engine supports queries containing typed variables (such as NounPhrase) and string-processing functions (such as "head(X)" or "ProperNoun(X)") as well as standard query terms.


In this alignment, for every canonical phone given by the lexicon, there were zero or more corresponding realized phones.

all three use the lower-bound stress profile as a starting place, combined with full phonemic information for the syllable with primary stress.


By and large, confusions occur (at least in humans) between phones with phonological features in common (e.g., (Miller and Nicely, 1955)).

the common thread among this research is the notion that acoustic models should be sensitive to sub-phonetic information.


In the next two sections, we relax these assumptions by applying extensions of this method to connected speech.

composing R•C•D will give the graph of all potential complete hypotheses in this space.


Alternatively, it could recognize the broad class as a first pass, and then refine this into a full phonemic analysis for stressed syllables in a second pass, at the cost of multiplying passes through the speech data.

in order to match this to dictionary pronunciations, we train a confusion matrix on all realized/canonical phone pairs, to obtain P (dictionary phone|transcribed phone); these confusion probabilities are encoded as a finite state transducer C. C is derived by computing the strength of all correspondences between the phonetic transcription of what was actually said at the phone level and the canonical pronunciation of the corresponding words.


We noticed that an independent work uses a different graphical modeling approach, dependency network, to understand the relationships between implicit measures and explicit satisfaction while user were conducting their web searches and viewing results, and then uses decision tree to predict user satisfaction with results (Fox et al., 2005).

the content of each web page crawled is saved in the news repository.


This means that given other evidence (RSS inf o, topic inf o, relevance score and readability score), the system won't improve its prediction of the document much by observing these actions.

we begin with a description of the user study in Section 2, followed by some preliminary data analysis on the data collected in Section 3.


Table 6 presents a list of some trigram features for second-level classification (one or multiple values) for Spouse.

most research efforts in the Question Answering community have focused on factoid questions and successful Question Answering systems tend to have similar underlying pipelines structures (Prager et al., 2004;Xu et al., 2003;Hovy et al., 2000;moldovan et al., 2000).


The comparison (see Table 10) shows that our approach which is based on data-driven patterns and statistics can achieve comparable performance.

the percentage of test questions for which the system has chosen a correct sentence as the answer.


Inclusion of whole answer sentences in Web query formulation generally does not return the answer pattern that we seek in this work.

over 80% of the questions in the Remedia corpus correspond to answer sentences that have a single occurrence only.


We think that our tag-word pairs are effective because they are selected by a linguistically meaning- 7 Significance was measured with the randomized significance test described in (Yeh, 2000).

6 These results are reported in Table 3.


A frequency cutoff of 200 yields a vocabulary of 508 pairs, while a cut-off of 20 yields 4242 pairs, 3734 of which comprise new words.

our method performs better on semantic labels.


Table 5 also shows the results for this collection of texts.

in order to lemmatize German nouns it is necessary to first categorize them into the classes defined above.


When a document is displayed, all of the entities detected in the document are listed down the left side of the document viewer.

source documents were selected based on the availability of manual annotation.


Since lowcount features are filtered out, the sparsity of pronoun events prevent many compound features (e.g., conjunction of syntactic and distance features) from being trained in the Chinese system, which explains why the syntactic features do not help Chinese pronouns.


This results in a total of 20 different models (10 for each classifier), for which we computed role-specific F-scores.

(2) System performance for a given role depends on how easily it is confused with other roles.


This result is consistent with anecdotal evidence suggesting that morphological analysis becomes less helpful as corpus sizes increase.

examination of the alignments did not reveal any obvious reason for this effect.


The remainder of the corpus (about 50,000 sentences) is used for training.

morphological analysis of the Czech input can potentially be used to improve the translation model by exploiting the other types of correspondences we have mentioned.


Although this combination will need to be determined for each language pair, the general approach outlined here should provide benefits for any MT system involving a highly inflected language.

we expect that the work described here could be generalized to highly inflected output languages by doing morphological analysis on both the input and output languages.


If we choose (θ 0 , θ 1 ) = (20, 15) the classifier labels every instance of the training data with +1.

the next section illustrates this point further.


The next section illustrates this point further.

it could be used to find a point estimate which provides a certain tradeoff between specificity and sensitivity, or operating point.


We proposed a novel sketch-based procedure for constructing sample contingency tables.

well-understood maximum likelihood estimation (MLE) techniques can be applied to sketches (or to traditional samples) to estimate word associations.


Therefore, we expectâ M LE has smaller MSE thanâ M F .

but this is probably infeasible at Web scale.


The sample expectations are (Siegrist, 1997),  The margin-free estimator and its variance arê For the multinomial simplification, we havê where "r" indicates "sample-with-replacement."

in our experiments, we will impose a floor to make sure that the sample contains at least 20 documents for each term.


Their system resembled one of two baseline methods that we submitted without our focus feature.

in Task 2, the inputs are the set of relevant sentences, so that the program does not see the entire documents.


This computation shows an average output for all participants of about 6,500 sentences and a median of 6,981 -out of a total of 8,343 sentences.

on the 2003 data, when we set T at .9, we found that we had a precision of .71 and a recall of 0.98, indicating that about 6% of the sentences were quite similar to some preceding sentence (See Figure 2).


A reasonable inference in this case is that the 'workers' are LOCATED at the  Relation Instance Shortest Path in Undirected Dependency Graph In Figure 2 we show three more examples of the LOCATED (AT) relationship as dependency paths created from one or two predicate-argument structures.

the SVM algorithm will induce lower weights for features occurring in longer paths, resulting in a linear separator that works irrespective of the size of the dependency paths.


In BLANC we incorporate multiple references in a manner similar to the ROUGE metric family.

in this case, the number of matches is M = n • m. Step 1 has worst case time and space complexity of O(m • n).


It is much larger than the seminar announcements data set.

second, information from the main task can inform the subtask.


As in the previous work, System stime etime location speaker overall WHISK (Soderland, 1999) 92.6 86.1 66.6 18.3 65.9 SRV (Freitag, 1998) 98.5 77.9 72.7 56.3 76.4 HMM (Frietag & McCallum, 1999) 98.5 62.1 78.6 76.6 78.9 RAPIER (Califf & Mooney, 1999) Peshkin and Pfeffer (2003).)

in the above w t is the word at position t, T t is the POS tag at position t, w ranges over all words in the training data, and T ranges over all Penn Treebank part-of-speech tags.


To perform joint decoding at test time, we form the composition of the individual CRFs, viewed as finitestate transducers.

dynamic conditional random fields (Sutton et al., 2004) extend linear-chain CRFs in the same way that dynamic Bayes nets (dean & Kanazawa, 1989) extend HMMs.


Part of the contribution of the current work is to suggest that joint decoding can be effective even when joint training is not possible because jointly-labeled data is unavailable.

we need to transfer regularities learned from a well-studied subtask, such as finding person names in newswire text, to a new, related task, such as finding names of speakers in email seminar announcements.


Some of our best results were obtained with relatively small libraries (just over 200,000 entries) of short bi-phrases.

hypotheses are extended by filling the first available uncovered position in the target sentence; each extended hypotheses is then inserted in the stack corresponding to the updated number of covered source words.


Most of the word-level confidence measures which have been presented in the literature so far are either based on relatively simple translation models such as IBM-1 (Blatz et al., 2003) or make use of information provided by the SMT system such as N -best lists or word graphs (Blatz et al., 2003;.

the experiments were performed on three different language pairs.


we determine the maximal translation probability of the target word e over the source sentence words: where f 0 is the "empty" source word (Brown et al., 1993).


For example, in the IBM Models (Brown et al., 1993), each word t i independently generates 0, 1, or more words in the source language.

we evaluated this combined translation model on the blank-filling task for various settings of the mixture coefficients λ.


When given a large amount of training data for a particular word with reasonably clear sense distinctions, existing systems perform fairly well.

for the traditional word-sense disambiguation task, data must be hand-labeled, and is therefore often too scarce to allow for discriminative training.


It is worth repeating that accuracies on this task are artificially low since in many cases a single word can be translated to many different words with the same meaning.

this definition is quite noisy: a word b i might have been aligned with a arbitrarily; or, b i might be a word that itself corresponds to a multiword translation in S. Thus, we also align the sentences in the T → S direction, and require that each b i in the phrase aligns either with a or with nothing.


We also demonstrate that contextfree parsers can train effectively on relatively little training data, and are more robust to domain shift for shallow parsing tasks than has been previously reported.

despite having very similar performance, our shallow parser and the Charniak context-free parser are likely making complementary predictions about the shallow structure that can be exploited for further improvements.


Of the three estimatesP 2,1 ,P 2,2 , andP 2,3 , onlyP 2,1 retains the information that the noun is plural and the verb is singular.

row 3 gives the accuracy of the reranking approach, when applied to n-best output from the model in row 2.


Some preliminary investigation of this suggests that we see much higher gains when using generic features than these more specific ones, but we leave a thorough investigation of this to future work.

we see our final model as combining the strengths of two complementary approaches.


In step 6, the spine for new, the first conjunct of the predicate coordination, is generated.

we are not satisfied with the score on oracle, which is not good enough for post-processing, i.e.


All four questions were able to be generated for only 75 (about half) of the words.

the antonym question requires matching a word with an antonymous word.


The prior measures the probability of a word appearing, ignoring the presence or absence of a prime.

in Church's case, the prior and adaptation probabilities are estimated as follows.


only make explicit claims about comprehension, not about production.)

even the data in a spontaneous speech corpus such as Swtichboard can be expected to involve a certain amount of self-monitoring (speakers listen to themselves and correct themselves if necessary).


Brill and Resnik (1994) used the supervised transformation-based learning method and lexical and conceptual classes derived from Word-Net, achieving 82% precision on 500 randomly selected examples.

there is also an all-way coordination, where the conjunct is part of the whole, as in Securities and Exchange Commission.


It would transform shaken/v confidence/n 1 in/p markets/n 2 into confidence/n 1 in/p markets/n 2 shaken/v.

due to the principle of economy of expression, the first instance of production has been compressed out by means of ellipsis.


10 By contrast, we choose a large and already consensual terminology to identify the true terms in our candidate sets.

mESH (2004), whereas "t cell response" is not.


We observe that backward constituent alignment-based models (1-4) perform similarly to word-based projection models (the F-score ranges between 0.40 and 0.45).

so, a randomly selected sentence pair may evoke novel frames or novel senses of already existing frames (e.g., the "greeting" sense of hail which is currently not listed in FrameNet).


Handling word merge/split errors improve the performance, which is expected.

coverage of the lexicon is far less important.


We applied all four of these configurations to our unseen development/test data, in order to see if we could further differentiate their performances.

mRR, used in the TREC Q&A evaluations, is the reciprocal rank of the first correct answer (or sentence, in our case) to a given question.


Our goal is to build a question-focused sentence retrieval mechanism using a topic-sensitive version of the LexRank method.

while LexRank outperforms the baseline system on the first two clusters both in terms of MRR and TRDR, their performances are not substantially different on the third cluster.


the second experiment, answer probability, begins to explore whether opinion information can be  used in an answer generator.


Partial answers either (1) lack the specificity needed to constitute a full answer (e.g., "before May 2004" partially answers the question When was the Kyoto protocol ratified?

in addition, the annotations include several attributes, including the intensity (with possible values low, medium, high, and extreme) and the source of the private state.


For microaveraging, scores were calculated by computing the nugget match scores over all nuggets for all questions.

tREC 2003 was the first formal evaluation of definition questions; as methodologies are refined, the margin of error should go down.


Linguistically motivated syntactic units such as noun phrases (Zhai, 1997), head-modifier pairs (Fagan, 1987;Strzalkowski et al., 1996) and subjectverb-object triples (Katz and Lin, 2003) have also been integrated in information retrieval.

it has been argued that NLP and especially deep syntactic analysis is still too brittle and ineffective (Katz and Lin, 2003).


We distinguish between token layers, type layers and annotation layers.

we use the mean of the total reciprocal ranks (MTRR), cf.


If T were infinite, every query log token would need to be considered, taking too much time.

typing errors can be divided into two groups (Kucich, 1992): typographic errors and cognitive errors.


It is no wonder manual feedback shows excellent and stable performance if enough relevant documents are available, hence the challenge is how it keeps high performance with less amount of manual relevance judgment.

if we can use more relevant documents in query expansion, the likelihood of selecting query terms achieving high search improvement increases.


The relative ranking of these results is not altogether surprising given the relationships between inferencing and LSA and between inferencing and dialogue.

the differences between these two methods may be entirely attributable to the amount of training they received.


different religions), the tendency to form topic-specific clusters intensifies regardless of factors that otherwise could have impact this tendency, such as the co-occurrence window size.

we introduce an unsupervised algorithmic framework based on distributional data clustering, which generalizes previous initial works on this task.


Clusters of finer granularity still seem to capture fundamental, though more focused, themes.

we tested the IB-SI method as well, following the adaptation scheme to the CP setting described by Marx et al, with a fixed value of its parameter, (with higher values convergence did not take place in all experiments).


After the completion of the quantitative evaluation, the participants were given a questionnaire which consisted of 14 questions related to the participants' perception of their experience using VJ such as the degree of satisfaction, frustration, and embarrassment.

we have empirically found, however, that slightly non-causal and quite localized estimates of the vowel category probability is sufficient to achieve user satisfaction.


Natural spoken language is often regarded as the obvious choice for a human-computer interface.

in summary, the results from our study suggest that users without any prior experience were able to perform basic mouse based tasks using the Vocal Joystick system with relative slowdown of four to nine times compared to a conventional mouse.


We present a novel voice-based humancomputer interface designed to enable individuals with motor impairments to use vocal parameters for continuous control tasks.

it is more efficient to decouple feature extraction from pattern recognition, as is shown in Figure 3.


The system interprets an input sentence by taking syntactic dependency and synonymous expression into consideration for matching it with the KB.

this is combined with top-down knowledge sources of metadata and hand-crafted questions.


Various factors of the entire system might influence the performance in real dialogue which is evaluated by the log data.

these approaches are not applicable to general docu-  In this paper, we propose a dialogue strategy to clarify the user's query and constrain the retrieval for a large-scale text knowledge base, which does not have a structure nor any semantic slots.


For some state-action pairs our task does not allow the accurate specification of the resulting state.

for example, the system must learn to choose between email reading styles of reading back the full email first, reading a summary of the email first, or prompting the user with the two choices of reading styles.


We have demonstrated that it is possible to learn a statistical model of verb semantic argument structure directly from unannotated text.

our goal is to learn a model which relates a verb, its semantic roles, and their possible syntactic realizations.


One section of the WSJ was annotated by both annotators independently to calculate inter-annotator agreement.

the parser outputs a derivation where "largest" is combined with "shareholder", and then with the possessive construction, yielding the wrong semantic interpretation.


We suspect this is partially due the tuples that fail to be paired up with their counterpart.

semantic role labeling identifies the dependencies between predicates and their arguments, allowing a more precise measurement of sentence similarity.


In the latter task, of all the WordNet-based measures, only that proposed by Jiang and Conrath outperforms the best distributional conceptdistance measures.

we adopt this method and this test data, but whereas Hirst and Budanitsky used wordNet-based semantic measures, we use distributional measures Distrib word and Distrib concept .


Given a set of word-pairs, humans can rank them in order of their distance-placing near-synonyms on one end of the ranking and unrelated pairs on the other.

the matrix that is created after one pass of the corpus, which we call the base WCCM, although noisy (as it is created from raw text and not senseannotated data), captures strong associations between categories and co-occurring words.


Values greater than 1 for each of these ratios indicate results better than random guessing.

as we pointed out in Mohammad and Hirst (2005), such aggregate co-occurrence vectors are expected to be noisy because they are created from data that is not sense-annotated.


As with the broad-coverage experiments, all of the black-box smoothing techniques do significantly better than the RF baseline.

an advantage of loglinear interpolation is that we can tune loglinear weights so as to maximize the true objective function, for instance BLEU; recall that our translation model is itself loglinear, with weights set to minimize errors.


Certain changes, such as improved root identification and final punctuation attachment, were very common across the corpus.

each feature is also conjoined with the direction and distance of the parent, e.g.


SMR is a first-pass translation performed on the source corpus, which converts it into an intermediate representation, in which source-language words are presented in an order that more closely matches that of the target language.

• The word classes were determined using 'mkcls', a freely-available tool with GIZA++.


Such behaviour of the classifier is to be expected, given the 'in-between' nature of mediated entities.

the overwhelming majority of mistakes (1,453, 56.1% of all errors) in the three-way classification stems from classifying as mediated entities that are in fact new (table 6).


), and many of the contrastive statements have no negative connotation at all and simply state a (value-free) difference between approaches.

these latter are marked up, slightly unusually in comparison to other citation indexers, because we believe they function as important referents comparable in importance to formal citations.


For this reason, Kübler (2005) and Maier (2006) tested a version of Negra which contained information of the original attachment site of these discontinuous constituents.

to investigate the first issue, the Stanford Parser (Klein and Manning, 2003b), a state-of-the-art probabilistic parser, was trained with both lexicalized and unlexicalized versions of the two treebanks (Experiment I).


C3 forces coordination arguments to the left of a conjunction to have commas in between.

our best results on the development set were achieved using the feature set of McDonald et al.


Finally, P1 allows selective projective parsing: we can, for instance, forbid the crossing of "Noun-Determiner" dependencies if we add the corresponding label type to P (see Section 4.4 for more details of P ) .

the CoNLL data differs slightly from the original Alpino treebank as the corpus has been part-of-speech tagged using a Memory-Based-tagger (Daelemans et al., 1996).


The CoNLL data differs slightly from the original Alpino treebank as the corpus has been part-of-speech tagged using a Memory-Based-Tagger (Daelemans et al., 1996).

a significant difference is only observed between our baseline and our constraintbased system.


The constraints are chosen based on the two criteria: (1) adding them to the base constraints (those added in advance) would result in an extremely large program, and (2) it must be efficient to detect whether the constraint is violated in y.

if we want a constraint which forbids duplicate subjects we need to provide additional labels to fall back on.


Another possible reason for the accuracy is the constraints placed by unification-based grammars.

given a set W of words and a set F of feature structures, an HPSg is formulated as a tuple, g = L, R , where  Previous studies (Abney, 1997;Johnson et al., 1999;Riezler et al., 2000;Malouf and van Noord, 2004;Kaplan et al., 2004;) defined a probabilistic model of unification-based grammars including HPSg as a log-linear model or maximum entropy model (Berger et al., 1996).


The DLA strategy we adopt in this research is based on supertagging, which is a simple instance of sequential tagging with a larger, more linguistically-diverse tag set than is conventionally the case, e.g., with part-of-speech tagging.

our particular interest in this paper is in the latter of these two, that is the development of methods for automatically expanding the lexical coverage of an existing precision grammar, or more broadly deep lexical acquisition (DLA hereafter).


died word 3 The new information prompted them to call off the search.

we created a dataset annotated for lexical reference, using a sample of sentence pairs (texthypothesis) from the 1st Recognising Textual Entailment dataset.


While this greedy strategy is not guaranteed to find the optimal solution, it finds a reasonable approximation (Cohen et al., 1999).

our ultimate goal is to automatically construct a graph that encodes ordering between temporal segments.


It is important to note that the classification for each pair is performed independently and is not guaranteed to yield a globally consistent order.

8 The transitive reduction of a graph contains on average 20.9 nodes and 20.5 edges.


Note, however, the local ordering procedure is not guaranteed to produce a consistent TDAG, and thus the local classifier cannot be used on its own to produce a valid TDAG.

temporal analysis has been a focus of linguistic research for quite some time.


Also, it has been showed that active learning can be effectively applied to the SVMs (Schohn and Cohn, 2000;Tong and Koller, 2000).

figure 1 illustrates the overall system architecture.


The baseline for these experiments is 56.2% for the majority class (nonhumorous).

as before, the table shows the effect of humor adjusted for speaker, the effect of speaker adjusted for humor and also the effect of interaction between humor and speaker, on each of the acousticprosodic features.


When we use the most relevant data chunk (about 20 million words) to re-rank the N -best list, 36 chunks of data will be used at least once for 919 different sentences, which accounts for about 720 million words in total.

besides comparing the distributed language model re-ranked translations with the model-best translations, we also want to compare the distributed LM with the the standard 3-gram and 4-gram language models on the N -best list re-ranking task.


Figure 5 (right) investigates the relationship between the search effort and BLEU score for A* and bottom-up CYK parsing, both with pruning.

in Figure 1(b), there is one solid cell (corresponding to the Model 1 Viterbi alignment) in each column, falling either in the upper or lower outside shaded corner.


The exploration step of the A* search is to expand the translation hypothesis of a substring by combining with neighboring translation hypotheses.

this system makes use of the A* heuristic, and our speed/performance curve shows that the heuristic allows us to achieve higher BLEU scores with the same amount of computation.


We analyzed the relation between the number of impression keywords used for the transliteration and the average rank of correct answers, by varying the threshold for the number of top impression keywords used.

we calculate P (w |K) as a product of P (w i |k j ), which is the probability that w i is selected as an impression keyword given k j .


Transliteration candidates that include many characters derived from the impression model are preferred.

we evaluated our transliteration method experimentally.


In Tables 4 and 5, the average rank of correct answers for the control is the same for all assessors.

each test word can be classified into one of the following five categories: products, companies, places, persons, or general words.


Although we used a smoothing technique for characters not in the model, the result was not satisfactory.

because different Kanji characters convey different meanings and impressions, characters must be selected carefully.


This substitution occurs frequently in the transliteration of foreign names in Chinese.

but the vectors are very different and should be constructed in quite different ways.


So, Chinese only allows [n, N] in coda position, and stop consonants in coda position are frequently lost; Stanford is transliterated as sitanfu, with the final /d/ lost.

we ended up having a list of less than 200 English names, shown in the second column of Table 6 (All).


Values different from NP unambiguously represent theme-marking sentences, whereas the NP value may stand for both: theme-marking as well as not thememarking sentence.

boosTexter achieves in all cases a much higher precision which is preferable over the higher recall provided by TiMbL.


Both these clustering decisions are locally optimized.

the BESTCUT algorithm) by linking them to their antecedent with the best coreference confidence.


Finally, the remaining two parts, which are typically noun phrases, of the raw pattern become entity components.

in most cases, the cost of manually crafting patterns is simply transferred to that for constructing a large amount of training data, which requires tedious amount of manual labor to annotate text.


a situation where a large number of equivalent structures with the same energy are accessible.

in terms of the coordinates of its atoms) to the free energies of the corresponding structures.


as enzymes that catalyze certain reactions) depends on their three-dimensional structure.


Whether there are significant qualitative and quantitative differences of PP and CP priming with respect to choice of derivation type -which would point out processing differences in comprehension vs. production priming -will be a matter of future work.

previous work has demonstrated that priming effects on different linguistic levels are not independent (pickering and Branigan, 1998).


Annotations contain errors, and conversions such as the one to CCG may add further error.

the latter ones represent temporary states, encoding the syntactic process.


This is not simply because markovization increases the number of parameters: markovization with L = 2 already beats basic models that have much higher L and far more parameters.

6 In future work, we would like to apply this technique to split nonterminals gradually, by initially requiring high-entropy parse forests on the training data and slowly relaxing this constraint.


Adding these up and canceling the two L × N terms, the INHERIT model has degrees of freedom.

whenever the training likelihood began to converge, we manually and globally increased L, simply doubling or tripling it (see "clone all" in Table 3 and Fig.


The hope was that the learning process could then make finer distinctions and learn more fine-grained information.


As has been previously observed and exploited in the NLP literature (Pang and Lee, 2004;Agarwal and Bhattacharyya, 2005;Barzilay and Lapata, 2005), the above optimization function, unlike many others that have been proposed for graph or set partitioning, can be solved exactly in an provably efficient manner via methods for finding minimum cuts in graphs.

we then define the strength agr of the agreement link corresponding to the reference as: The free parameter α specifies the relative impor-7 One subtlety is that for the purposes of mining agreement cues (but not for evaluating overall support/oppose classification accuracy), we temporarily re-inserted into our dataset previously filtered speech segments containing the term "yield", since the yielding of time on the House floor typically indicates agreement even though the yield statements contain little relevant text on their own.


In the United States, for example, governmental bodies are providing and soliciting political documents via the Internet, with lofty goals in mind: electronic rulemaking (eRulemaking) initiatives involving the "electronic collection, distribution, synthesis, and analysis of public commentary in the regulatory rulemaking process", may "[alter] the citizen-government relationship" (Shulman and Schlosberg, 2002).

we approach the problem of classifying references by representing each reference with a wordpresence vector derived from a window of text surrounding the reference.


Determining whether or not a speaker supports a proposal falls within the realm of sentiment analysis, an extremely active research area devoted to the computational treatment of subjective or opinion-oriented language (early work includes Wiebe and Rapaport (1988), Hearst (1992), Sack (1994), and Wiebe (1994); see Esuli (2006) for an active bibliography).

we extracted from GovTrack all available transcripts of U.S. floor debates in the House of Representatives for the year 2005 (3268 pages of transcripts in total), together with voting records for all roll-call votes during that year.


We use speech segments as the unit of classification because they represent natural discourse units.

the systematic identification of instances of agreement can, as we have discussed, be a powerful tool for the development of intelligently selected weights for links between speech segments.


First, U.S. congressional debates contain very rich language and cover an extremely wide variety of topics, ranging from flag burning to international policy to the federal budget.

as an alternative to using same-speaker links, we tried a speaker-based approach wherein the way we determine the initial individual-document classification score for each speech segment uttered by a person p in a given debate is to run an SVM on the concatenation of all of p's speech segments within that debate.


This paper addresses the problem of opinion summarization by considering the creation of simple opinion summaries like those of figure 1.

stRip's performance is better than the baselines when trained on MPQA src (improvement not statistically significant, p > 0.20) and even better when trained on the full MPQA corpus, which includes the unlabeled NPs (improvement over the baselines and the former stRip run statistically significant).


Sentiment analysis is concerned with extracting attitudes, opinions, evaluations, and sentiment from text.

to our setting, in the semisupervised case there is no training phase -the algorithm receives all examples (labeled and unlabeled) at the same time together with some distance or cost function and attempts to find a clustering that optimizes a given measure (usually based on the distance or cost function).


For both training and testing, coreference resolution algorithms rely on feature vectors for pairs of noun phrases that encode linguistic information about the NPs and their local context.

opinion corpora in general, and our corpus in particular, contain no coreference information about general NPs.


From the example Japanese sentence (1) in the digital camera domain, the SA system extracts a sentiment representation as (2), which consists of a predicate and an argument with positive (+) polarity.

to assign a polarity to each proposition, polar atoms in the lexicon are compared to the proposition.


In the example 7above, utsukushii is the head of the tree, while those initial clauses in (9) to (11) below are not.

besides the conflicting cases, there are many more cases where a polar clause does not appear in the polar context.


According to the evaluation of the MT method (Kanayama et al., 2004), highprecision sentiment analysis had been achieved using the polar atoms and patterns, where the system never took positive sentiment for negative and vice versa, and judged positive or negative to neutral expressions in only about 10% cases.

sentiment Analysis (sA)  is a task to recognize writers' feelings as expressed in positive or negative comments, by analyzing unreadably large numbers of documents.


Many probabilistic approaches to modeling sequences have relied on directed graphical models, also known as Bayesian networks (BN), 1 in particular hidden Markov models (Rabiner, 1989) and conditional Markov models (McCallum et al., 2000).

it achieves 34.8% WER on the iCSi corpus, which is indicative of the difficulty involved in processing meetings automatically.


As shown, the words assigned to the LDA topic state are representative of content words and are grouped into broad semantic topics.

again, we selected the lowest perplexity model as the final model.


Instead, we employ an exponential decay strategy where we update the current mixture distribution by linearly interpolating it with the posterior topic distribution given the current word.

as motivated in section 5.2, allowing for contextdependent interpolation weights based on topic labels may yield significant improvement for both perplexity and WER.


Most of the previous work on topic-mixture models focuses on in-domain adaptation using large amounts of matched training data.

experiments with static model interpolation yielded a perplexity and relative word error rate (WeR) reduction of 7.1% and 2.1%, respectively, over an adapted trigram baseline.


Probabilistic LSA (PLSA) has been used to decompose documents into component word distributions and create unigram topic models from these distributions.

an instance of the word "return" may be assigned to a syntactic state in "to return a", but classified as a topic keyword in "expected return for".


Unlike vocabulary selection techniques that separate domain-independent words from topicspecific keywords using word collocation statistics, HMM-LDA classifies each word instance according to its context.

the algorithm creates a Markov chain whose stationary distribution matches the expected distribution of the state and topic labels for each word in the training corpus.


In the case of unsupervised HMM taggers, the lexicon provides constraints on the probability tables p(w i |t i ) and p(t i |t i−1 , t i−2 ).

2 The average precision of the analyzer-generated POS-sets against the oracle is 56.46%.


Using this method, the candidate correction of a misspelled word might be successfully picked.

recent advances in printed document digitization and processing led to large scale digitization efforts of legacy printed documents producing document images.


Semi-supervised clustering (Wagstaff et al., 2001) may be used to perform classification by the use of labeled and unlabeled examples, but it encounters the same problem of partially supervised classification that model order cannot be automatically estimated.

the number of senses of each English word varies from 3 to 11.


LPU algorithm can deal with only one-class classification problem.

compute the row-normalized matrix T by T uu and T ul are acquired by splitting matrix T after the |D L |-th row and the |D L |-th column into 4 sub-matrices.